Magnetic resonance imaging (“MRI”) can produce clinical scans with a spatial resolution of millimeters, however, many disease processes, including Alzheimer's disease, traumatic brain injury and stroke, develop at the cellular level, all at a scale on the order of micrometers, which is about three orders of magnitude below imaging resolution available today.
Therefore, there may be a need to develop a framework with the ability to resolve and quantify nominally invisible (e.g., far below the standard MRI resolution) tissue complexity at the mesoscopic scale. This scale, which can range between a fraction of a μm to tens of μm, can be intermediate (“meso”), between the microscopic scale of molecules (e.g., nanometers), where the nuclear magnetic resonance signal originates, and the macroscopic scale of MRI (e.g., millimeter resolution of clinical MRI scanners). The mesoscopic scale can be the scale of cellular tissue architecture, which can make tissues specific, complex, and radically different from a mere solution of proteins in water.
Currently, there exists a challenge to bridge the meso-macro gap, and to become sensitive to vital changes in structural and functional tissue parameters at the mesoscopic scale. These changes occur, for example, in progressive atrophy of neuronal and glial cells and their processes, loss of myelin sheath, beading, and other specific changes in packing geometry of axons and dendrites and glial cells. This spatial resolution challenge can be fundamental, and overcoming it by the brute-force improvements in hardware alone can yield only incremental advances at an ever-increasing cost. Current clinical MRI systems operate at the physical and physiological bounds on field strength, neuronal stimulation, and energy deposition. These bounds can limit the signal-to-noise ratio, which, combined with bounds on the acquisition time of about 30-60 minutes, can result in a typical imaging voxel size of about a cubic millimeter, far exceeding the desired mesoscopic scale.
A basic principle utilized for probing tissue microarchitecture at the mesoscopic scale can be based on the molecular diffusion, measured with dMRI. Distance covered by diffusing water molecules during typical measurement time, t, the diffusion length L(t) approximately 1-30 μm, is generally commensurate with cell dimensions. Therefore, the dMRI measurement can be inherently sensitive to the tissue architecture at the most relevant biological length scales. However, it has been long realized that interpreting dMRI results in terms of the mesoscopic tissue architecture in each imaging voxel can be a very challenging inverse problem. Quantifying mesoscopic tissue parameters within each imaging voxel, and identifying their relative importance, is currently a much discussed, and generally so far an unresolved topic. For example, in one of the recent approaches (See, e.g., References 5, 7 and 8), the mesoscopic structure of brain white matter (“WM”) has been quantified in some biologically meaningful terms, but only for those regions in which the neuronal fibers, the constituent units of white matter, can be parallel. This can be a serious limitation for clinical applications, since the presence and crossing of non-parallel fibers can be ubiquitous in the brain (See, e.g., Reference 3). This limitation is especially pronounced in gray matter regions, where the distribution of dendritic and axonal fiber orientations in each imaging voxel can be especially broad (See, e.g., Reference 4).
dMRI technique(s) can provide the possibility to reconstruct, to a certain extent, the geometry of neuronal fibers in brain white matter, which can stretch across many voxels, and connect different brain regions and the brain to the body. A field of multi-voxel connectivity, often referred to as fiber tracking or tractography, has long been complementary to the field of quantifying mesoscopic tissue parameters within each voxel. For the most part, tractography can be based on following the direction of the principal eigenvalue of the diffusion tensor in each voxel, either deterministically or probabilistically. Numerous tractography procedures have been put forth to connect these directions into macroscopic streamlines resembling white matter (e.g., axonal) fibers. Since its introduction in 1999, tractography has become a significant field attracting neuroscientists and computer scientists interested in developing procedures of how to best draw these streamlines. However, certain challenges still remaining for this field are a relative inaccuracy of the resulting streamlines and its lack of robustness with respect to the measurement noise, especially for the less pronounced white matter tracts, and voxels containing multiple fiber directions. Hence, despite its promise, fiber tracking has not yet achieved wide usage for the diagnostics and the pre-surgical planning.
Combining mesoscopic modeling with tractography is not well suited for the conventional fiber tracking procedures, which aim at drawing macroscopic fiber streamlines, since the models and parameters of the mesoscopic tissue architecture (e.g., as the ones outlined above) generally do not have a place in the tracking procedures operating at the scale of voxel dimensions, far exceeding the mesoscopic scale. Therefore, the tractography can use oversimplified models of tissue architecture as a voxel-wise pre-processing step, or, alternatively, tractography results have been utilized merely as a way to segment the tissue and to identify the WM regions, and then, subsequently, to feed these segmentation results to the local voxel-wise models (e.g., the so-called tractometry). (See e.g., Reference 6). In this way, quantifying the mesoscopic tissue structure, and outlining the macroscopic connectivity have remained distinctly separate processing steps.
Thus, it may be beneficial to provide exemplary system, method and computer-accessible medium that can facilitate both the sub-voxel mesoscopic quantification, and the multi-voxel connectivity (e.g., tractography), and which can overcome at least some of the deficiencies described herein above.